Digital images and designs conventionally use an RGB (i.e., red/green/blue) color model. More particularly, digital images conventionally use an additive color model in which red, green, and blue light are added together at various levels to reproduce an array of colors. In creating and/or manipulating digital images, color mixing (e.g., mixing two or more colors) is important. One problem with conventional color mixing systems that mix colors defined within an RGB color model (hereinafter “RGB colors”) is that resulting output colors (i.e., colors that result from combining RGB values of selected colors) differ from colors resulting from mixing actual pigments of color. For example, mixing a yellow RGB color with a blue RGB color results in gray instead of green. Such results lead to disadvantages and problems with mixing colors for digital image purposes (e.g., creating artwork, designs, editing digital photographs). For example, by not providing expected and easily predictable mixed output colors, a user cannot readily and easily mix colors with a predictable and desired result when using many conventional color mixing systems. Furthermore, the user may waste significant time testing and mixing different colors before achieving (e.g., generating) a desired color. As a result, a user may become frustrated when using such conventional mixing models and may avoid using digital image creation and manipulation systems that do not provide predictable color mixing.
Some more physically correct models exist such as the Kubelka-Munk model (hereinafter “KM model”). The KM model computes reflectance of the colors by combining absorption and scattering coefficients of pigments at different wavelengths. The KM model represents each color by concentrations of several pre-defined pigments (e.g., conventionally more than eight). Accordingly, the KM model often requires more than three channels (e.g., more than a blue, a green, and a red channel) to store the color. Furthermore, mixing calculations using the KM model involves complex formula evaluation, and the images (i.e., the colors of the images) need to be converted to an RGB model before rendering the mixed color. As a result, mixing with the KM model requires a considerable amount of processing power and memory (e.g., computation overhead). For devices with limited memory space and slower processing power, such a color model becomes cumbersome and, in some instances, unusable. Another problem with the KM model is that any conversion from the KM model to the RGB model is irreversible. The irreversibility of the conversion within the KM model may lead to users losing progress (e.g., work) made on digital images if, for example, the digital image is converted on accident or prior to the user completing the work.
Another conventional more physically correct model is the red-yellow-blue model (hereinafter, “RYB model”), which uses a predefined mixing output of primary colors (i.e., red, yellow, and blue) and interpolates between them to achieve output mixed colors. However, the RYB gamut (e.g., range and/or scope) is significantly smaller than the RGB color space. Thus, significantly fewer colors are available within the RYB gamut in comparison to the RGB color space. Furthermore, the RYB uses a nonlinear conversion method that prevents generating (e.g., creating) pure white and/or pure black colors. As a result, a user's color palette (e.g., range of colors) is significantly limited in comparison to using real pigment paints or RGB colors. Such limitations provide significant disadvantages to users (e.g., artists, photographers, animators, etc.) using the RYB model to mix colors for use within their digital images. Moreover, similar to the KM model, converting colors from the RYB model to the RGB model is irreversible.
Accordingly, these and other disadvantages exist with respect to conventional systems and methods for digitally mixing colors.